Potential for using soil particle-size data to infer geological parent material in the Sydney Region
Bibliographic record
Abstract
Ecological communities are more than assemblages of species. In assessing the presence of many ecological communities, interpretation of soil properties and associated parent material has become a definitive component under environmental legislation worldwide, and particularly in Australia. The hypothesis tested here is that the geological parent material of a soil sample can be determined from particle size fraction data of the Marshall soil texture diagram. Supervised statistical classifiers were built from data for four particle-size fractions from four soil landscape publications. These methods were modified by taking into account possible autocorrelation between samples from the same site. The soil samples could not be classified with certainty as being derived from Wianamatta Group Shale or Hawkesbury Sandstone parent material. The classification of alluvial/fluvial-derived soils was no better than chance alone. A good classifier using four-fraction compositional data could not be built to determine geological parent material. Hence, the three size fractions of the Marshall soil texture diagram are insufficient to determine the geological parent material of a soil sample.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".